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Using microwave emissivities for the estimation of global land surface heat fluxes Carlos Jimenez1, Catherine Prigent1, Filipe Aires2 1LERMA, 2LMD, <carlos.jimenez@obspm.fr> Observatoire de Paris, France IPSL, Université Paris VI, France 2nd WS on Remote Sensing and Modelling of Surface Properties, Toulouse, 6/2009 Motivation • Estimating land surface heat fluxes is challenging as the fluxes do not have a unique signature that can be remotely or directly detected. Some possibilities: [e.g. monthly latent fluxes August 93] 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes (a) using observations to infer the properties of the atmosphere and surface needed to derive the fluxes by physically based formulations e.g. (Fisher J., 2007, Rem. Sens.Envir.) (b) using observations to force ‘complex’ land surface model, e.g GSWP-2 (Dirmeyer P. (2006), BAMS) 4.Applications (c) assimilating observations into a coupled land-atmosphere model e.g NCEP reanalysis (Kalnay. E. (1996), BAMS) <carlos.jimenez@obspm.fr> 2 Motivation e.g. latent flux annual climatologies (1993-1995) for different ecosystems 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications • a large spread of values is observed when comparing the existing global estimates of land surface heat fluxes. <carlos.jimenez@obspm.fr> 3 Motivation • Several global data analysis activities are conducted in the frame of GEWEX to complete the description of the energy and water cycle. • Most products are now being worked on (clouds, aerosols, radiative fluxes, precipitation, ocean surface turbulent fluxes, water vapour, temperature, and ozone) apart from the land surface heat fluxes. 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications The LandFlux initiative of the GEWEX Radiation Panel (GRP): • Objectives: to develop the needed capabilities to produce a global, multi-decadal surface turbulent flux data product. • Agenda: 1st workshop in Toulouse, May 2007. 2nd workshop in Melbourne, Aug 2009. (http://www.gewex.org/projects-GRP.htm) <carlos.jimenez@obspm.fr> 4 Estimating fluxes: land surface models • Most global heat flux estimates are coming from coupled/off-line SAVT schemes with some surface parameters derived from remote sensing data (e.g LAI), many others from approximate relationships with vegetation, soil type or climate regime. e.g. 1993 sensible and latent flux annual mean from two off-line models with similar forcing ISBA GSWP-2 B0 baseline run 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes ORCHIDEE 4.Applications • Not easy to calibrate/tune the comprehensive parameterizations of the land models when doing the transition from the local/regional to the global scale. <carlos.jimenez@obspm.fr> 5 Estimating fluxes: satellite observations • There is satellite data with temporal and spatial resolutions compatible with surface models and with expected sensitivity to the land fluxes. e.g. monthly mean values for June 93 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes AVHRR reflectances [visible and near-IR] 4.Applications ISCCP skin temperature [diurnal cycle, thermal infrared] [Aires et al. (2004), JGR] <carlos.jimenez@obspm.fr> 6 Estimating fluxes: satellite observations • There is satellite data with temporal and spatial resolutions compatible with surface models and with expected sensitivity to the land fluxes. e.g. monthly mean values for June 93 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes ERS backscattering (active microwave) 4.Applications SSM/I emissivities [passive microwave] [Prigent et al. (2006), BAMS] AVHRR ISCCP global correlation with NCEP latent fluxes R <carlos.jimenez@obspm.fr> 0.79 0.76 ERS SSMI 0.75 0.83 7 Combining observations and model fluxes • Some of these data are presently not exploited in the calibration/ development of the land surface models, as there is so far no easy way to integrate these data into the models. 1.Motivation • Our proposal: to use an statistical model to provide the mapping between the observations and the land fluxes. 2.Satellite and fluxes 3.Linking satellite and fluxes Satellite data Statistical model LSM fluxes 4.Applications The relationship between observational data and fluxes is not prescribed, but derived from the statistical analysis of a global dataset of coincident multifrequency satellite observations and land model fluxes. <carlos.jimenez@obspm.fr> 8 Combining observations and model fluxes Example Applying the statistical modeling with a suite of coincident: 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications Satellite observations: Global fluxes: • ISCCP thermal infrared land skin • GSWP-2 Global Soil Wetness temperature [mean value and the Project exercise amplitude of the diurnal cycle]. • SSM/I microwave emissivities [vert/hor polarized at 19, 37 and 85 GHz]. • ERS microwave backscattering [at 20° and 45° incident angles] • AVHRR reflectances [visible and near-infrared]. - 15 LSMs driven in off-line mode using global meteorological forcing in 19861995. - using fluxes from a multi-model analysis (average across the individual models) and two French participating models (ISBA and ORCHIDEE). • NCEP/NCAR reanalysis - 50 years record frozen global data assimilation system with a couple landatmosphere scheme. [ monthly means in 1993-1995, 0.25o x 0.25o] <carlos.jimenez@obspm.fr> [Jimenez et al. (2009), JGR] 9 Methodology Phase 1 The statistical models learn the global relationships between observations and fluxes. 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications [e.g. calibrating with Feb-May-Aug-Nov 93] Phase 2 The statistical models map the observations into fluxes using the learned global relationships. [estimating fluxes for rest of 93-95] <carlos.jimenez@obspm.fr> 10 Applications • Producing land heat fluxes • the observation-driven fluxes are a new product combining the information from the observations and the land surface model. e.g. GSWP-mult and NCEP latent fluxes in August 1995 r ~ 0.9, RMSE ~ 25 W/m2 2.Satellite and fluxes GSWP 1.Motivation 4.Applications NCEP 3.Linking satellite and fluxes original estimated • Is the new product merging observations and land model outputs a better estimate? Global evaluation/validation of heat fluxes remains very challenging. <carlos.jimenez@obspm.fr> 11 Applications • Model development • for specific regions and times there may be no consistency between the LSM fluxes and the statistical model fluxes: this can be used to diagnose potentially anomalous LSM fluxes. e.g. averaged fluxes in 2°x2° box around Tapajos Forest [54°W-3°S] 2.Satellite and fluxes sensible 1.Motivation 4.Applications latent 3.Linking satellite and fluxes original estimated - in this example the estimated fluxes seem to partition more according with the expected fluxes that some of the original fluxes. <carlos.jimenez@obspm.fr> 12 Applications • Model development • for specific regions and times there may be no consistency between the LSM fluxes and the statistical model fluxes: this can be used to diagnose potentially anomalous LSM fluxes. e.g. averaged fluxes in 2°x2° box around Tapajos Forest [54°W-3°S] 2.Satellite and fluxes sensible 1.Motivation 3.Linking satellite and fluxes original estimated 4.Applications - large fluxes at the end of 1995 “corrected” in the estimated fluxes (fluxes anomaly due an anomaly in GSWP radiative forcing). • caution when interpreting the differences, as they can also be related to problems with the observational data or the statistical model. <carlos.jimenez@obspm.fr> 13 Applications • Assimilation • the observations mapped into state variables by the statistical model could be integrated into the LSM by standard variational assimilation schemes. Cost function to combine information from the observations and the LSM: 1.Motivation background term 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications LSM satellite-derived statistical model error • there exist techniques to calculate Ri and give more weights to the statistical model predictions when there are more reliable. [Aires (2004), JGR] • as the statistical model was calibrated with the LSM outputs, we force consistency between LSM and satellite-derived state variables and minimize problems trying to assimilate exogenous inputs (bias correction, pdf matching, ….) <carlos.jimenez@obspm.fr> 14 Summary Microwave emissivities can be a significant source of information at the time and spatial resolutions required for the production of a global climatology of land surface heat fluxes. We propose an statistical analysis to globally map a dataset of multifrequency observations (including the emissivities) into land surface model (LSM) heat fluxes. The satelite-driven fluxes can be considered as a new product that merges information from the observations and the LSMs. The methodology can also be used for: 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications - model development as discrepancies between the original LSM state variable and the satellite-driven variable can potentially identify LSM problems. - including satellite observations into assimilation schemes minimizing the problems related to assimilating exogenous inputs. <carlos.jimenez@obspm.fr> 15 Summary • For further details: [Jimenez, C., C. Prigent, and F. Aires, Toward an estimation of global land surface heat fluxes from multisatellite observations, J. Geophys. Res., 114, D06305, 2009.] [Aires, F., and C. Prigent, Toward a new generation of satellite surface products?, J. Geophys. Res., 111, D22S10, doi:10.1029/2006JD007362, 2006.] 1.Motivation 2.Satellite and fluxes 3.Linking satellite and fluxes 4.Applications [Prigent, C., F. Aires, and W. B. Rossow, Land surface microwave emissivities over the globe for a decade, Bul. Amer. Meteorol. Soc., doi:10.1175/BAMS-8711-1573, 1573-1584, 2006.] [Aires, F., C. Prigent, W. B. Rossow, Temporal interpolation of global surface skin temperature diurnal cycle over land under clear and cloudy conditions, J. of Geophy. Research, 109, D06214, doi:10.1029/2003JD003527, 2004.] [Aires, F., Neural network uncertainty assessment using Bayesian statistics with application to remote sensing: 1. Network weights, J. of Geophy. Research, D10303, doi:10.1029/2003JD004173, 2004.] Acknowledgments. All data and model teams are acknowledged by making their observations and model runs available. <carlos.jimenez@obspm.fr> 16